package net.bwie.realtime.jtp.dws.log.job;

import net.bwie.realtime.jtp.dws.log.utils.AnalyzerUtil;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.table.annotation.DataTypeHint;
import org.apache.flink.table.annotation.FunctionHint;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.TableEnvironment;
import org.apache.flink.table.functions.TableFunction;
import org.apache.flink.types.Row;

import java.util.List;

/**
 * 搜索关键词实时统计，其中使用ikAnalyser分词器进行分词，采用Flinksql的方式进行数据处理
 *
 * @Author: FuHe
 * @Date: 2025/5/20
 */
public class JtpTrafficSearchKeywordMinuteWindowDwsJob {
    public static void main(String[] args) {
        // 表执行环境
        TableEnvironment tabEnv = getTableEnv();
        // 输入表  input 映射到kafka消息队列
        createInputTable(tabEnv);
        // 数据处理
        Table reportTable = handle(tabEnv);
        // 输出表  output  映射到clickhouse表
        createOutputTable(tabEnv);
        // 保存数据
        saveToClickhouse(tabEnv, reportTable);
    }

    private static void saveToClickhouse(TableEnvironment tabEnv, Table reportTable) {
        // 注册表
        tabEnv.createTemporaryView("report_table", reportTable);
        // 插入数据
        tabEnv.executeSql("INSERT INTO dws_traffic_search_keyword_window_report_clickhouse_sink\n" +
                "SELECT\n" +
                "    DATE_FORMAT(window_start_time, 'yyyy-MM-dd HH:mm:ss') AS window_start_time,\n" +
                "    DATE_FORMAT(window_end_time, 'yyyy-MM-dd HH:mm:ss') AS window_end_time,\n" +
                "    keyword,\n" +
                "    keyword_count,\n" +
                "    ts\n" +
                "FROM report_table");
    }

    private static void createOutputTable(TableEnvironment tabEnv) {
        tabEnv.executeSql("CREATE TABLE dws_traffic_search_keyword_window_report_clickhouse_sink (\n" +
                "    window_start_time STRING COMMENT '窗口开始日期时间',\n" +
                "    window_end_time STRING COMMENT '窗口结束日期时间',\n" +
                "    keyword STRING COMMENT '搜索关键词',\n" +
                "    keyword_count BIGINT COMMENT '搜索关键词被搜索次数',\n" +
                "    ts BIGINT COMMENT '数据产生时间戳'\n" +
                ") WITH (\n" +
                "    'connector' = 'clickhouse',\n" +
                "    'url' = 'jdbc:clickhouse://node103:8123/jtp_log_report',\n" +
                "    'table' = 'dws_traffic_search_keyword_window_report',\n" +
                "    'username' = 'default',\n" +
                "    'password' = '',\n" +
                "    'format' = 'json'\n" +
                ")");
    }

    private static Table handle(TableEnvironment tabEnv) {
        // 获取搜索词和搜索时间
        Table searchLogTable = tabEnv.sqlQuery("SELECT\n" +
                "    page['item'] AS full_word\n" +
                "     , row_time\n" +
                "FROM dwd_traffic_page_log_kafka_source\n" +
                "WHERE page['item'] IS NOT NULL\n" +
                "  AND page['last_page_id'] = 'search'\n" +
                "  AND page['item_type'] = 'keyword'");
        tabEnv.createTemporaryView("search_log_table", searchLogTable);
        // 使用自定义udtf函数，对搜索词进行分词
        tabEnv.createTemporarySystemFunction("ik_analyzer_udtf", IkAnalyzerFunction.class);

        Table wordLogTable = tabEnv.sqlQuery("SELECT\n" +
                "    full_word, \n" +
                "    keyword, \n" +
                "    row_time\n" +
                "FROM search_log_table,\n" +
                "     LATERAL TABLE(ik_analyzer_udtf(full_word)) AS T(keyword)");

        tabEnv.createTemporaryView("word_log_table", wordLogTable);
        // 设置窗口进行分组聚合计算
//        Table reportTable = tabEnv.sqlQuery("SELECT\n" +
//                "    TUMBLE_START(row_time, INTERVAL '1' MINUTES) AS window_start_time,\n" +
//                "    TUMBLE_END(row_time, INTERVAL '1' MINUTES) AS window_end_time, \n" +
//                "    keyword,\n" +
//                "    count(keyword) AS keyword_count,\n" +
//                "    UNIX_TIMESTAMP() * 1000 AS ts\n" +
//                "FROM word_log_table\n" +
//                "GROUP BY TUMBLE(row_time, INTERVAL '1' MINUTES ), keyword");
        Table reportTable = tabEnv.sqlQuery(
                "SELECT window_start AS window_start_time,\n" +
                "       window_end AS window_end_time,\n" +
                "       keyword,\n" +
                "       count(keyword) as keyword_count,\n" +
                "       UNIX_TIMESTAMP() * 1000 as ts\n" +
                "FROM TABLE(\n" +
                "             TUMBLE(TABLE word_log_table, DESCRIPTOR(row_time), interval '1' minutes)\n" +
                "         ) t1\n" +
                "GROUP BY window_start, window_end, keyword");


        return reportTable;
    }

    private static void createInputTable(TableEnvironment tabEnv) {
        tabEnv.executeSql("CREATE TABLE dwd_traffic_page_log_kafka_source\n" +
                "(\n" +
                "    common MAP<STRING, STRING> COMMENT '公共环境信息',\n" +
                "    page   MAP<STRING, STRING> COMMENT '页面信息',\n" +
                "    ts     BIGINT,\n" +
                "    row_time AS TO_TIMESTAMP(FROM_UNIXTIME(ts / 1000, 'yyyy-MM-dd HH:mm:ss.SSS')),\n" +
                "    WATERMARK FOR row_time AS row_time - INTERVAL '0' MINUTE\n" +
                ") WITH (\n" +
                "    'connector' = 'kafka',\n" +
                "    'topic' = 'dwd-traffic-page-log',\n" +
                "    'properties.bootstrap.servers' = 'node101:9092,node102:9092,node103:9092',\n" +
                "    'properties.group.id' = 'gid_dws_traffic_search_keyword',\n" +
                "    'scan.startup.mode' = 'earliest-offset',\n" +
                "    'format' = 'json',\n" +
                "    'json.fail-on-missing-field' = 'false',\n" +
                "    'json.ignore-parse-errors' = 'true'\n" +
                ")");
    }

    private static TableEnvironment getTableEnv() {
        // 环境属性设置
        EnvironmentSettings settings = EnvironmentSettings.newInstance()
                .inStreamingMode()
                .useBlinkPlanner()
                .build();
        TableEnvironment tabEnv = TableEnvironment.create(settings);
        // 配置属性设置
        Configuration configuration = tabEnv.getConfig().getConfiguration();
        // 时区设置
        configuration.setString("table.local-time-zone", "Asia/Shanghai");
        // 并发设置
        configuration.setString("table.exec.resource.default-parallelism", "1");
        // 状态保存时间
        configuration.setString("table.exec.state.ttl", "5s");
        // 返回值
        return tabEnv;
    }

    @FunctionHint(output = @DataTypeHint("ROW<keyword STRING>"))
    public static class IkAnalyzerFunction extends TableFunction<Row> {
        public void eval(String fullWord) throws Exception {
            // 中文分词
            List<String> list = AnalyzerUtil.ikAnalyzer(fullWord);
            // 循环遍历输出
            for (String keyword : list) {
                collect(Row.of(keyword));
            }
        }
    }

}
